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Contribution to the understanding of how principal component analysis–derived dietary patterns emerge from habitual data on food consumption
BACKGROUND: Principal component analysis (PCA) is a widely used exploratory method in epidemiology to derive dietary patterns from habitual diet. Such dietary patterns seem to originate from intakes on multiple days and eating occasions. Therefore, analyzing food intake of study populations with dif...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6411615/ https://www.ncbi.nlm.nih.gov/pubmed/29529145 http://dx.doi.org/10.1093/ajcn/nqx027 |
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author | Schwedhelm, Carolina Iqbal, Khalid Knüppel, Sven Schwingshackl, Lukas Boeing, Heiner |
author_facet | Schwedhelm, Carolina Iqbal, Khalid Knüppel, Sven Schwingshackl, Lukas Boeing, Heiner |
author_sort | Schwedhelm, Carolina |
collection | PubMed |
description | BACKGROUND: Principal component analysis (PCA) is a widely used exploratory method in epidemiology to derive dietary patterns from habitual diet. Such dietary patterns seem to originate from intakes on multiple days and eating occasions. Therefore, analyzing food intake of study populations with different levels of food consumption can provide additional insights as to how habitual dietary patterns are formed. OBJECTIVE: We analyzed the food intake data of German adults in terms of the relations among food groups from three 24-h dietary recalls (24hDRs) on the habitual, single-day, and main-meal levels, and investigated the contribution of each level to the formation of PCA-derived habitual dietary patterns. DESIGN: Three 24hDRs were collected in 2010–2012 from 816 adults for an European Prospective Investigation into Cancer and Nutrition (EPIC)–Potsdam subcohort study. We identified PCA-derived habitual dietary patterns and compared cross-sectional food consumption data in terms of correlation (Spearman), consistency (intraclass correlation coefficient), and frequency of consumption across all days and main meals. Contribution to the formation of the dietary patterns was obtained through Spearman correlation of the dietary pattern scores. RESULTS: Among the meals, breakfast appeared to be the most consistent eating occasion within individuals. Dinner showed the strongest correlations with “Prudent” (Spearman correlation = 0.60), “Western” (Spearman correlation = 0.59), and “Traditional” (Spearman correlation = 0.60) dietary patterns identified on the habitual level, and lunch showed the strongest correlations with the “Cereals and legumes” (Spearman correlation = 0.60) habitual dietary pattern. CONCLUSIONS: Higher meal consistency was related to lower contributions to the formation of PCA-derived habitual dietary patterns. Absolute amounts of food consumption did not strongly conform to the habitual dietary patterns by meals, suggesting that these patterns are formed by complex combinations of variable food consumption across meals. Dinner showed the highest contribution to the formation of habitual dietary patterns. This study provided information about how PCA-derived dietary patterns are formed and how they could be influenced. |
format | Online Article Text |
id | pubmed-6411615 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-64116152019-03-14 Contribution to the understanding of how principal component analysis–derived dietary patterns emerge from habitual data on food consumption Schwedhelm, Carolina Iqbal, Khalid Knüppel, Sven Schwingshackl, Lukas Boeing, Heiner Am J Clin Nutr Original Research Communications BACKGROUND: Principal component analysis (PCA) is a widely used exploratory method in epidemiology to derive dietary patterns from habitual diet. Such dietary patterns seem to originate from intakes on multiple days and eating occasions. Therefore, analyzing food intake of study populations with different levels of food consumption can provide additional insights as to how habitual dietary patterns are formed. OBJECTIVE: We analyzed the food intake data of German adults in terms of the relations among food groups from three 24-h dietary recalls (24hDRs) on the habitual, single-day, and main-meal levels, and investigated the contribution of each level to the formation of PCA-derived habitual dietary patterns. DESIGN: Three 24hDRs were collected in 2010–2012 from 816 adults for an European Prospective Investigation into Cancer and Nutrition (EPIC)–Potsdam subcohort study. We identified PCA-derived habitual dietary patterns and compared cross-sectional food consumption data in terms of correlation (Spearman), consistency (intraclass correlation coefficient), and frequency of consumption across all days and main meals. Contribution to the formation of the dietary patterns was obtained through Spearman correlation of the dietary pattern scores. RESULTS: Among the meals, breakfast appeared to be the most consistent eating occasion within individuals. Dinner showed the strongest correlations with “Prudent” (Spearman correlation = 0.60), “Western” (Spearman correlation = 0.59), and “Traditional” (Spearman correlation = 0.60) dietary patterns identified on the habitual level, and lunch showed the strongest correlations with the “Cereals and legumes” (Spearman correlation = 0.60) habitual dietary pattern. CONCLUSIONS: Higher meal consistency was related to lower contributions to the formation of PCA-derived habitual dietary patterns. Absolute amounts of food consumption did not strongly conform to the habitual dietary patterns by meals, suggesting that these patterns are formed by complex combinations of variable food consumption across meals. Dinner showed the highest contribution to the formation of habitual dietary patterns. This study provided information about how PCA-derived dietary patterns are formed and how they could be influenced. Oxford University Press 2018-02 2018-02-26 /pmc/articles/PMC6411615/ /pubmed/29529145 http://dx.doi.org/10.1093/ajcn/nqx027 Text en © 2018 American Society for Nutrition. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits noncommercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Research Communications Schwedhelm, Carolina Iqbal, Khalid Knüppel, Sven Schwingshackl, Lukas Boeing, Heiner Contribution to the understanding of how principal component analysis–derived dietary patterns emerge from habitual data on food consumption |
title | Contribution to the understanding of how principal component analysis–derived dietary patterns emerge from habitual data on food consumption |
title_full | Contribution to the understanding of how principal component analysis–derived dietary patterns emerge from habitual data on food consumption |
title_fullStr | Contribution to the understanding of how principal component analysis–derived dietary patterns emerge from habitual data on food consumption |
title_full_unstemmed | Contribution to the understanding of how principal component analysis–derived dietary patterns emerge from habitual data on food consumption |
title_short | Contribution to the understanding of how principal component analysis–derived dietary patterns emerge from habitual data on food consumption |
title_sort | contribution to the understanding of how principal component analysis–derived dietary patterns emerge from habitual data on food consumption |
topic | Original Research Communications |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6411615/ https://www.ncbi.nlm.nih.gov/pubmed/29529145 http://dx.doi.org/10.1093/ajcn/nqx027 |
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